Neural network architecture for the estimation of drivers' route choice

被引:0
|
作者
Kyung Whan Kim
Dae Hyon Kim
Hyun Yeal Seo
机构
[1] College of Engineering,Urban Engineering Major, Division of Construction Engineering
[2] Gyeongsang National University,Environment & Regional Development Institute
[3] Yosu National University,Transportation Engineering Major, Division of Transportation & Logistics System Engineering
[4] Gyeongsang National,Department of Urvan Engineering, Graduate School
关键词
artificial neural network; customized neural network; logit model; route choice;
D O I
10.1007/BF02829155
中图分类号
学科分类号
摘要
The artificial neural network has recently been applied in many areas including transport engineering and planning. However, since the general neural network considers all the listed variables in a batch, the network seemed to be unsophisticated. A more sophisticated neural network model therefore had to be developed. In this study, a sophisticated neural network model was developed for drivers' route choice model. Its performance was then compared with the performance of the Logit model. For the development of the neural network model, two different neural network models-the general neural network model and the customized neural network model whose architecture is similar to the Logit models-were considered. The results showed that the customized neural network could perform better than other models in terms of prediction accuracy and goodness-of-fit.
引用
收藏
页码:329 / 336
页数:7
相关论文
共 50 条
  • [41] Social implications of coexistence of CAVs and human drivers in the context of route choice
    Jamroz, Grzegorz
    Akman, Ahmet Onur
    Psarou, Anastasia
    Varga, Zoltan Gyorgy
    Kucharski, Rafal
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [42] Modelling drivers' compliance and route choice behaviour in response to travel information
    Hussein Dia
    Sakda Panwai
    Nonlinear Dynamics, 2007, 49 : 493 - 509
  • [43] Study on the impact of freeway toll rate on drivers’ route choice behavior
    Huan N.
    Yao E.
    Yang Y.
    Lu T.
    Advances in Transportation Studies, 2019, 47 : 21 - 34
  • [44] A link based network route choice model with unrestricted choice set
    Fosgerau, Mogens
    Frejinger, Emma
    Karlstrom, Anders
    TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2013, 56 : 70 - 80
  • [45] The Best Neural Network Architecture
    Kuri-Morales, Angel Fernando
    NATURE-INSPIRED COMPUTATION AND MACHINE LEARNING, PT II, 2014, 8857 : 72 - 84
  • [46] Loop architecture neural network
    Zhang, Yunjun
    Chen, Zongzhi
    Dianzi Kexue Xuekan/Journal of Electronics, 1995, 17 (06): : 638 - 642
  • [47] Spiking Neural Network Architecture
    Montuschi, Paolo
    COMPUTER, 2015, 48 (10) : 6 - 6
  • [48] Route Choice Estimation Using Cell Phone Data
    Taghipour, Homa
    Shafahi, Yousef
    2016 5TH INTERNATIONAL CONFERENCE ON TRANSPORTATION AND TRAFFIC ENGINEERING (ICTTE 2016), 2016, 81
  • [49] The Impact of Route Choice Modeling on Dynamic OD Estimation
    Cipriani, Ernesto
    del Giudice, Andrea
    Nigro, Marialisa
    Cantelmo, Guido
    Viti, Francesco
    2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, : 1483 - 1488
  • [50] Iterative update of route choice proportions in OD estimation
    Yousefikia, Mohammad
    Mamdoohi, Amir Reza
    Noruzoliaee, Mohamadhossein
    PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT, 2016, 169 (01) : 53 - 60